基于正交试验-人工神经网络模型的镁合金薄板加热拉伸工艺优化  

Optimization of Heated Tensile Process for Magnesium Alloy Sheet Based on Orthogonal Experiment-artificial Neural Network Model

作　　者：张华[1] 陈丰[1] 夏显明[1] 

机构地区：[1]安徽科技学院工学院,安徽凤阳233100

出　　处：《热加工工艺》2011年第22期5-7,共3页Hot Working Technology

基　　金：安徽省教育厅自然科学基金资助项目(KJ2009B073Z);安徽科技学院校级重点建设学科资助项目(AKXK20102-5);安徽科技学院引进人才团队合作项目(ZRC2008226)

摘　　要：设计了正交试验表,在万能试验机上对AZ31镁合金试样进行了在线加热拉伸试验,对正交试验结果进行了极差分析和方差分析,并且通过人工神经网络模型优化,得到了AZ31镁合金薄板的最佳拉伸工艺参数,即拉伸温度300℃,拉伸速度10 mm/min,变形量20%。该结论为指导AZ31镁合金薄壁拉伸件的生产提供了理论依据。Orthogonal experiment table for AZ31 alloy was designed.Online heating tensile tests were carried out using universal testing machine.Range analysis and variance analysis of orthogonal experimental results were carried out.The process was optimized by artificial neural network model.The best tensile process parameters for AZ31 alloy are achieved,that is,tensile temperature is 300 ℃,tensile speed is 10 mm/min,deformation is 20%.The theoretical basis is provided for thin-walled tensile parts production of AZ31 alloy.

关 键 词：正交试验 极差分析 方差分析 在线加热拉伸 人工神经网络模型 

分 类 号：TG146.22[一般工业技术—材料科学与工程]